Best Data analytics Training

Best Data analytics Training by Sapalogy offers you the best Data analytics training in nagpur for freshers and experience candidate in nagpur, with expert guidance and 100% guaranteed placement assistance. Our Data analytics training course covers every technical topics in details and the student is left with real-time hand on experience.

Our practical, Job-oriented training program will not only provide you with a certificate but also with knowledge equivalent to around 2+ years of field experience. We value your time as much as over. Hence we invented to train you in Data analytics in just 5 months.

All the topics will be covered in details and also include

  • Git hub
  • Snow flake
  • Chat gpt
  • Interview preparation
  • Case studies
  • 5+ Capstone project
  • Industry level projects
  • With 100% job opportunities guaranted program

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What is Data analytics ?

  • Data analytics is the process of analysing raw data in order to draw out meaningful, actionable insights.
  • Data analytics help business to understand the target market fastes, increases sales, reduce costs, increase revenue and allow better problem solving.
  • The demand for data analyst and scientist in india is at an all time high, with a projected an million job opening by 2026.
Data analytics Training
Data analytics Training
  • Sapalogy cover all the data analytics skills to get you hired in 2024.
  • Sapalogy taining provides Data analytics training in offline and online mode. Support with real time Data analytics project based training.
  • IT background, non IT background, freshers, experience can start their career in Data analytics irrespective of their background.
  • Sapalogy is the best training institute in nagpur with the 100% job opportunities.

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Roadmap to learn Data Analytics with Sapalogy

1. Introduction
  • What is Data analytics ?
  • Data Analytics course
  • Data Analytics certification
  • Data Analytics jobs in india
  • Data Analytics jobs in nagpur
2. Define Objectives and Scope
  • Clearly outline the goals and objectives of your data analytics initiative.
  • Identify the key questions you want to answer or the problems you want to solve.
  • Define the scope of your analysis, including the data sources and time period.
3. Data Collection and Integration
  • Identify relevant data sources and gather the necessary datasets.
  • Cleanse and preprocess the data to ensure accuracy and consistency.
  • Integrate data from different sources to create a comprehensive dataset for analysis.
4.Exploratory Data Analysis (EDA)
  • Perform EDA to understand the characteristics of the data.
  • Identify patterns, trends, and outliers that may impact the analysis.
  • Use visualization techniques to communicate insights effectively.
5. Modeling and Analysis
  • Select appropriate analytical models based on the nature of the problem.
  • Train and validate models using historical data.
  • Apply the models to new data for predictive or descriptive analysis.
6. Interpretation and Communication
  • Interpret the results of the analysis in the context of the business objectives.
  • Clearly communicate findings to both technical and non-technical stakeholders.
  • Provide actionable insights and recommendations based on the analysis.
7. Continuous Improvement and Optimization
  • Establish mechanisms for continuous monitoring of data quality and model performance.
  • Iterate on the analysis based on feedback and changing business requirements.
  • Explore new data sources and advanced analytics techniques for continuous improvement.
8. Resume preparation
  • Include keywords
  • How to prepare resume
  • How to prepare resume for freshers.
  • Show impact
  • Include soft skills
  • Be unique

Key features of Data Analytics Training

  • 160+ Hours course duration
  • 100% Job oriented Training
  • Industry expert faculties
  • Free demo class available
  • Completed 200+ Batches
  • Certifaction guidance

Data Analyst Training syllabus

  • Introduction of python for data analysis
  • Data types in python
  • Pre-define function in python and its use
  • Input and output method
  • String data type and its operation
  • Integer data type and its operation
  • List data type and its operation
  • Tuple data type and its operation
  • Set data type and its operation
  • Dictionary data type and its operation
  • If, else and elif condition and nested condition
  • For loop and while loop and nested loop with condition
  • User-define function in python
  • List comprehension and lambda function
  • File handling in python
  • Operating System
  • Pandas for data cleaning
  • Numpy for data manipulation
  • Matplotlib for data visualization
  • Seaborn for data visualization
  • SciPy for statistics
  • Statistics for data analysis
  • Sci-kit learn for machine learning
  • Introduction of statistics
  • Stats Definition: I. Descriptive II. Inferential
  • Population and sample
  • Descriptive statistics
  • Distribution
  • Type of distribution
  • Inferential statistics
  • Hypothesis testing
  • Type-I error and Type-II error
  • Data transformation
  • Introduction of database
  • Type of database
  • MySQL introduction and installation
  • SQL commands
  • Database creation
  • Bulk data load
  • Basic queries in mysql
  • Stroe Procedure
  • View in SQL
  • Case in SQL
  • User-define function in database
  • Condition in database
  • Table constraint in table
  • Partition in SQL
  • Trigger in SQL
  • CTE, Recursive CTE
  • Regular expression in SQL
  • Window function
  • Window analytical function
  • Introduction of Power BI and its component
  • Power View: Creating and customizing visualization
  • Building interactive report and dashboard
  • Power Query: Data preparation and transformation using Power Query
  • ETL in Power BI
  • Power BI DAX: (Data Analysis Expression)
  • Integration of Power BI with database
  • Power BI Service: Sharing and publishing
  • Introduction of Excel: file format
  • Excel formatting: conditional formatting
  • Data validation
  • Pivot table and chart
  • Excel functions
  • Data Visualization techniques
  • Data Analysis Tool
  • Introduction of machine learning algorithms
  • Supervised Machine learning
  • Unsupervised Machine learning
  • Feature engineering and selection
  • Model building
  • Model evaluation and selection
  • GitHub
  • Snowflake
  • AWS
  • CHATGPT
  • Interview Preparation

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Skills covered by Sapalogy

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Upcoming Batch Schedule for Data Analyst Training

Sapalogy provides flexible timings to all our students. Here are the Data Analyst Training Classes in Nagpur Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timing.

CourseNew batch dateOffline OnlineEnquire now
Data analyticsStarts every weekNagpurIndiaEnquire now
Data scienceStarts every weekNagpurIndiaEnquire now
Python Starts every weekNagpurIndiaEnquire now

Can’t find a batch you are looking for you ?

Data Analyst certification

  • Sapalogy training certification will serve as proof that the courses were completed by Sapalogy.
  • The Data Analyst certification offered by Sapalogy will equip you with valuable skills, enhancing your competitiveness in the job market.
  • Sapalogy provides comprehensive guidance for your Data Analyst global certification, ensuring a 100% passing guarantee in examinations such as Data analytics Certification, Data Analyst Platform Development Certification, and various other global exams.
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Frequently asked questions

A Data Analyst is a professional who interprets and analyzes complex data sets to provide insights and support decision-making processes within an organization.

Key abilities include talent in programming languages (e.G., Python, R), statistical evaluation, data visualization, SQL, and a strong information of information manipulation and interpretation.

SQL (Structured Query Language) is crucial for Data Analysts as it allows them to query databases, retrieve relevant data, and perform various data manipulations for analysis.

Python is widely used in Data Analysis for its versatility and rich ecosystem of libraries (e.g., Pandas, NumPy, Matplotlib). It helps in data manipulation, statistical analysis, and creating visualizations.

While both roles deal with data, Data Analysts focus on interpreting and analyzing existing data to answer specific business questions, whereas Data Scientists often build predictive models and work on more complex, exploratory tasks.

Data visualization is crucial for conveying complex information in a clear and understandable way. It helps in identifying patterns, trends, and outliers in the data.

Exploratory Data Analysis is the process of visually and statistically exploring data sets to understand their characteristics, uncover patterns, and identify potential relationships between variables.

Data Analysts help companies make knowledgeable selections by presenting insights derived from records evaluation. They perceive traits, styles, and anomalies which can manual strategic picks.

Regression assessment is a statistical method used to recognize the relationship among based and unbiased variables. It helps in predicting the price of the based variable based totally completely at the values of impartial variables.

Feel free to ask